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Abstract

Background

Microarray technology has become highly valuable for identifying complex global changes
in gene expression patterns. The effective correlation of observed changes in gene
expression with shared transcription regulatory elements remains difficult to demonstrate
convincingly. One reason for this difficulty may result from the intricate convergence
of both transcriptional and mRNA turnover events which, together, directly influence
steady-state mRNA levels.

Results

In order to investigate the relative contribution of gene transcription and changes
in mRNA stability regulation to standard analyses of gene expression, we used two
distinct microarray methods which individually measure nuclear gene transcription
and changes in polyA mRNA gene expression. Gene expression profiles were obtained
from both polyA mRNA (whole-cell) and nuclear run-on (newly transcribed) RNA across
a time course of one hour following the activation of human Jurkat T cells with PMA
plus ionomycin. Comparative analysis revealed that regulation of mRNA stability may
account for as much as 50% of all measurements of changes in polyA mRNA in this system,
as inferred by the absence of any corresponding regulation of nuclear gene transcription
activity for these groups of genes. Genes which displayed dramatic elevations in both
mRNA and nuclear run-on RNA were shown to be inhibited by Actinomycin D (ActD) pre-treatment
of cells while large numbers of genes regulated only through altered mRNA turnover
(both up and down) were ActD-resistant. Consistent patterns across the time course
were observed for both transcribed and stability-regulated genes.

Conclusion

We propose that regulation of mRNA stability contributes significantly to the observed
changes in gene expression in response to external stimuli, as measured by high throughput
systems.

Background

Virtually all microarray studies to-date have measured changes in steady-state mRNA
levels by harvesting total cellular RNA and using it to generate probes through a
variety of strategies including end-labeling of purified mRNA [1], incorporating a label into the first strand cDNA made from mRNA [2], or attaching a T7 RNA polymerase promoter during cDNA synthesis, then labeling of
the resulting RNA [3]. More recently, several groups have demonstrated the feasibility of hybridizing metabolically
labeled mRNAs directly from nuclear run-on (NRO) reactions to nylon filter microarrays
in order to investigate nascent transcripts [1,4-6]. Schuhmacher et al. [5], in particular, used a B cell line carrying a conditional, tetracycline-regulated
myc gene, and found that myc induction resulted in only a small overlap in regulated mRNAs at 4 hours post-induction
when comparing polyA mRNA and NRO RNA on microarrays. This early work provided evidence
that transcriptional activation of genes does not necessarily lead to a corresponding
increase of their steady-state mRNA levels.

More recently, our laboratory has examined the relationship between newly transcribed
(NRO) RNA and polyA mRNA in a stress model using human non-small lung carcinoma H1299
cells. In response to a variety of stresses (ultraviolet light, heat shock, or prostaglandin),
we found that approximately half of the observed changes in mRNA levels of stress-regulated
genes were accompanied by a corresponding increase or decrease in gene transcription
as measured by NRO. The remaining half of stress-altered changes in gene expression
was largely attributable to changes in mRNA turnover, thus suggesting that, on a global
level, changes in mRNA turnover profoundly influence gene expression patterns [6].

Several questions, however, remained to be answered from these earlier studies. Since
in both the myc induction and stress experiments, as mentioned above, measurements of both newly transcribed
and polyA mRNAs were made at a single time point, there existed a reasonable possibility
of temporal disjunctions between the timing of mRNA new gene synthesis and the rates
of accumulation of mRNA in the cell. The second question remaining unanswered is whether
or not highly significant levels of mRNA stability regulation (> 50% of all measured
gene expression in the stress example) is common to different biological model systems.
In order to begin to address these questions we investigated changes at the levels
of transcription and total cellular mRNA abundance simultaneously across a time course
of activation using Jurkat T cells.

T-cell activation is one of the most widely studied models of cellular response to
exogenous stimulation. The initial events include rapid signaling via protein-protein
interactions, phosphorylation/dephosphorylation of target signaling molecules, and
release of Ca2+ from intracellular stores. Subsequent activation of signal transduction cascades culminates
in the implementation of gene expression patterns characteristic of the immune response.
Initial microarray studies using T cells have focused on gene expression changes occurring
several hours after activation [7-13], even though earlier work using more traditional methods had defined the commitment
period for T-cell activation, including alteration in gene expression patterns, as
occurring between 1–2 h after exposure to the activating agent [14]. In order to investigate these earlier gene expression changes we chose to examine
a time course of activation spanning the first hour after stimulation. While a recent
study by Garcia-Martinez et al. [15] in yeast using a similar approach demonstrated large shifts in mRNA stability following
a glucose-to-galactose shift, the work presented here is the first systematic accounting
of the changes in both gene transcription and mRNA stability in response to a major
cellular activation event over a defined time period in higher eukaryotes.

Results

T-cell commitment is believed to occur early during activation and therefore changes
in gene expression during the earliest stages of induction are of particular interest.
An experimental model using human Jurkat T cells activated with PMA and ionomycin
was used to investigate early changes in gene expression (up to one hour of stimulation),
focusing on both changes in mRNA transcription rates as well as polyA mRNA levels.
One hour was chosen in order to examine gene regulatory events occurring immediately
after activation and to avoid, for the most part, the influence of secondary gene
regulatory mechanisms taking place at later time points (e.g., increased synthesis
of transcription factors). NRO RNA was prepared from isolated cell nuclei (Methods)
and polyA mRNA from intact cells. Figure 1 shows an example of filter images obtained after hybridization of arrays using either
NRO RNA or polyA mRNA. Cells stimulated for 30 minutes exhibited moderate changes
in gene expression in the mRNA arrays. In contrast, NRO RNA arrays revealed rapid
and robust changes in transcription that were evident as early as 5 minutes following
induction. Unexpectedly, a careful analysis of all significant changes in gene expression
across the time course revealed that these early-response genes (as identified by
NRO) were a relatively small subset of all of the genes shown to be regulated (see
below).

In all, a total of 4608 genes, including sets of genes enriched for immune response
and signal transduction function, were polled (Fig. 2). Of these genes, 2386 showed significant regulation (p < 0.001, or Z ratio > ± 1.5)
during the time course of one hour post activation by either changing gene transcription
or polyA mRNA levels. These significantly regulated genes were chosen for further
analysis.

Figure 2. Distributions of significantly regulated genes in both polyA mRNA and nuclear run-on
(NRO) RNA. For this analysis, a gene was considered to be up- or down-regulated in
either polyA mRNA RNA or NRO RNA (Altered Gene Expression) if it was significantly
different from the baseline at any point during the time course of activation; all
other genes are in the 'Unaltered Gene Expression' category. The number and per cent
of genes in each of 8 possible regulatory categories are displayed.

The distribution and direction (increase, decrease, or no change) of significant changes
in gene expression either at the transcriptional level (NRO) or at the level of polyA
mRNA (whole-cell) are displayed in the table in Figure 2. The single most common expression event (55.2 %) was an up or down regulation at
one or more time points as measured in mRNA without a corresponding (either up or
down) regulation as measured by transcription at any time point. The second largest
group of regulated genes (27.4%) showed changes in transcription with no corresponding
change in polyA mRNA. Examples of genes dramatically up-regulated in this second class
included CD69, a type II transmembrane receptor involved in lymphocyte proliferation and a classic
marker of early T cell activation; PPP3C, the catalytic subunit of the calmodulin-activated phosphatase, calcineurin, which
plays a central role in signal transduction from the T cell receptor to the nucleus;
as well as several members of the JUN family of transcription factor immediate early response genes (Fig. 3A).

Figure 3. Comparison between polyA mRNA and nuclear run-on RNA of immediate early gene activation
in Jurkat T cells. A.1 Heatmap of relative gene expression intensities (Z scores). A.2 Graphical representation of the same data illustrating an immediately apparent up-regulation
of gene expression in NRO but not polyA mRNA. B. Single end-point PCR validation of up-regulation in polyA mRNA by 1 hour of a subset
of genes shown to be activated within 5 minutes by nuclear run-on RNA. C. Patterns of polyA mRNA and nuclear run-on RNA levels for NFKB1 and its inhibitor
(NFKBIA).

The final, relatively minor groups of regulated genes included genes which were regulated
at both the transcriptional and the polyA mRNA levels in either the same (8.4%) or
opposite directions (9%). The relatively low concordance between transcriptional production
of mRNA and its measured appearance in polyA mRNA levels was somewhat surprising,
although clear examples of coordinated step-wise production were noted for some key
genes, as for example, the early response genes EGR1 and ETR101 (previously shown to be induced at 30 minutes by phorbol ester treatment of a human
promyelocytic leukemia cell line [16] and, the apoptosis-related genes DAP (death-associated protein, mediator of interferon-gamma-induced apoptosis) and CASP3, as well as the immune response signal transducer and activator of transcription
(STAT) 6 (Fig. 3A).

Dramatic activation of immune response, immediate-early response genes, and apoptosis-related
genes was observed in the nuclear run-on RNA as early as 5 minutes following activation
(Fig. 3A). This group of genes included immediate-early response genes commonly up-regulated
during cellular activation (ETR101, Myb, Myc, and genes of the JUN and EGR families), genes specifically associated with an early response in immune cells (IL6, IL8, STAT4, STAT6, PPP3C, NFKBIA, IRF5, and CD69), as well as genes involved in regulating apoptosis (BCL2A1, CASP3, CASP9, CASP10, and DAP). Many of these same genes were eventually up-regulated in polyA mRNA later in the
time course and their increase in expression after one hour was independently validated
by single end-point PCR validation using GS320 technology (Fig. 3B).

A particularly interesting example of the dichotomy between transcription and changes
in polyA mRNA levels was seen in the production of the mRNAs encoding NFKB1 (NF-kappa
B), a key mediator of the transcriptional control of genes involved in the immune
response and acute phase reactions, and its inhibitor, NFKBIA (NF-kappa B inhibitor
A). Both NFKB1 and NFKBIA have previously been shown by microarray analysis to be
significantly induced in polyA mRNA between 3–4 hrs following phorbol or lectin activation
of either Jurkat or human peripheral blood lymphocytes [7,8,11,17]. As demonstrated here (Fig. 3C), the production of NFKB1 mRNA clearly increases between 30 minutes and one hour
at the transcriptional level without a detectable corresponding increase in polyA
mRNA during that time (subsequent PCR analysis did show some increase at the steady-state
level between 0 and 60 minutes for the NFKB1 gene but this increase failed to meet
the significance thresholds set for the microarray analysis). NFKBIA, on the other
hand, is rapidly induced transcriptionally to a maximum level by 30 minutes, returning
essentially to baseline within one hour. Meanwhile, NFKBIA steady-state levels can
be seen to gradually rise across the first hour of the time course. Analysis of the
dynamics of gene expression for NFKB1 and its inhibitor as deduced from conventional
microarrays might suggest that by one hour NFKB1 production had not yet begun (contradicted
by the NRO data here), and also that the mRNA for the inhibitor of NKFKB1 is steadily
increasing (when, in fact, it is clear from NRO data that virtually all increases
in the production of NFKBIA have concluded by one hour). These data provide a clear
example of how information from nuclear run-on microarrays can enhance studies of
gene activation and feedback mechanisms.

Consistency at each time point for genes regulated by activation-induced changes in
mRNA stability can be seen in a graph of the Z ratio differences (Methods) between
NRO and polyA mRNA calculated for all genes at all time points (Fig. 4A). These putative stability-regulated genes exhibited a very high degree of consistency
at all the time points measured. This replicability is further illustrated in the
heat map of clustered gene expression in Figure 4B in which the data for each gene has been independently normalized to its baseline
(0 time) level. Large numbers of genes as measured in the polyA mRNA are consistently
and strongly regulated following activation. Some of these changes are mirrored by
changes in gene transcription (NRO) but most are not.

Figure 4. Global comparison of gene expression changes in polyA mRNA and NRO RNA. A. At each time period indicated columns correspond to the values derived by subtracting
the Z ratio of NRO RNA from the Z ratio of polyA mRNA for every gene. Equivalency
between calculations varies around 0, data is aligned using a combined average index,
and is displayed from left to right to represent the highest average positive Z ratio
difference to the lowest average negative Z ratio difference. Columns in red correspond
to genes exhibiting significant differences (Z ratio difference values greater or
less than ± 1.5) in gene expression changes when comparing polyA mRNA and NRO RNA
for each gene at each time point. B. Hierarchical clustering of all significant changes in gene expression in either
NRO or polyA mRNA across the time course of activation. The median Z score data for
each gene is individually normalized to its baseline value in for the sake of this
comparison.

In order to compare changes in gene expression patterns at the transcriptional and
polyA mRNA levels in a systematic fashion, a simple barcode of 1, -1, or 0 was applied
to all significant changes in gene expression indicating up, down, or no change, respectively.
In addition, a value of -1, 0, or 1 (low, moderate, or high) was assigned to each
gene according to its relative intensity at baseline (0 time). An analysis of the
distribution of these gene expression patterns (Fig. 5) revealed that both at the transcriptional and the steady-state levels two thirds
of all genes were restricted to just one of 20 patterns (out of a possible number
of 729) and that half of these patterns were shared between the two groups. An interesting
distinction between the two groups was that whereas up-regulation from a moderately
high level of baseline expression was highly favored for new gene synthesis, down-regulation
from a moderately high level of baseline expression was very highly favored during
polyA mRNA regulation (Fig. 6). In fact, down-regulation, as a consistent trend, was much less common among transcriptional-regulated
than steady-state-regulated genes, with implications (see below) as to the roles these
modes of regulation play in concert for the control of gene expression.

Figure 5. Frequency distribution of gene expression patterns generated from polyA mRNA or NRO
RNA. The top 20 patterns for each method is shown. Significant changes in gene expression
were assigned a 1, -1, or 0 for up, down, or no change, respectively. In addition,
a value of -1, 0, or 1 (low, moderate, or high) was assigned to each gene according
to its relative intensity at baseline (0 time). The absolute numbers of genes in each
pattern are reported in the column labeled Top 20 and the percentage of those genes relative to all significantly regulated genes can
be found in the column labeled % Sig. Totals are as indicated at the bottom of each column. Filled-in boxes denote patterns
equally shared in the top 20 between both methods

Figure 6. Differential distribution of transcriptional and polyA mRNA up- (A) or down- (B) regulated
gene expression patterns during Jurkat T cell activation. The number of genes consistently
regulated (up or down at every time point) are correlated with their relative expression
baselines (Z score: high>1, 1>medium>-1, low<-1).

In order to confirm that stability regulation was in fact a reasonable explanation
for the observation that the expression levels of large numbers of genes were changing
at the whole cell but not the transcriptional level, a series of experiments were
carried out in which activation of Jurkat cells was carried out in the presence or
absence of the transcription inhibitor Actinomycin D. Analysis of polyA mRNA demonstrated
the strong inhibition of mRNA levels for genes previously shown to be transcriptionally
up-regulated (Fig. 7A). In contrast, large numbers of genes which were significantly regulated in polyA
mRNA but not in NRO RNA were not affected by Actinomycin D treatment (Fig. 7B &7C). Among these unaffected genes there was a bias towards presumptively de-stablized
genes (Fig. 7C) consistent with the earlier conclusion (Fig. 6B) that down-regulation is the predominant motif in overall polyA mRNA levels.

Figure 7. Persistence of stability-regulated changes in gene expression in the presence of Actinomycin
D. A. Effect of Actinomycin D on the (P+I)-induced changes in the expression patterns
of gene deemed to be transcriptionally regulated. Z ratio comparisons are made to
the baseline or unactivated state. B. Lack of an effect of Actinomycin D on the (P+I)-induced changes in the expression
patterns of genes deemed to be stability regulated. The absolute differences (Z diff)
between the activated and un-activated state for both polyA mRNA and NRO RNA for a
subset of these genes is shown. C. Comparison of the Z ratios for all significantly regulated genes in the presence
or absence of Actinomycin D showing no corresponding regulation in NRO RNA. Data is
sorted by polyA mRNA (without ActD) values and a significance threshold of Z ratios
> ± 1.5 was used for these calculations.

Although an examination of the functional classifications of stability regulated versus
transcriptionally regulated genes yielded no obvious trends, some biological pathways
appear to be differentially, and sometimes, exhaustively regulated by each type of
expression event. One example of this can be seen in the apoptotic pathways, which
are comprehensively regulated during the Jurkat activation scenario [16,18,19]. As can be seen from the pathway schematic illustrated in Figure 8, some major effectors of apoptosis such as CASP 3, 4, 8, 9, &10 (up-regulated) and BCL2 (down-regulated), are controlled by new gene synthesis while other factors such as
CASP 1, 6, &7 (up-regulated) and BCL2L2 (down-regulated) appear to be regulated by stability processes alone. Regardless of
the cellular rationale for regulating at the level of new gene synthesis or mRNA stability,
it is clear from these data that there is a strong internal coherence: genes regulated
by one mechanism do not crossover to the other within the time frame investigated.

Figure 8. Regulation of apoptotic pathways during T cell activation involves changes in gene
expression by both mRNA transcriptional and mRNA stability mechanisms. Genes colored
in red and blue were up- or down-regulated in polyA mRNA only. Genes colored in yellow
and purple were up- or down-regulated in NRO RNA. Pathway is from a Kegg map modified
in Genmapp2.0 [27].

Common patterns of transcription factor binding sites in the upstream promoter regions
of groups of genes whose transcription was significantly up-regulated were detected.
An enrichment of transcription factor-binding sites for both NFAT and NFκB was found
in the promoter regions of genes most significantly up-regulated at 1 hour as shown
in Figure 9. The frequency with which both NFAT- and NFκB-binding sites were found in the promoters
of this group of genes was significantly greater than that seen for other genes in
the array [e.g., genes down-regulated at this time point or genes selected at random
[supplemental data]. The discovery that NFAT- and NFκB-binding sites were enriched
in the promoters of these genes was not unexpected, since these transcription factors,
along with AP-1 components Fos and Jun, constitute the major transcription factors
involved in the early stages of T-cell activation. Indeed, the frequency of genes
significantly up-regulated in NRO RNA and enriched for AP-1 binding sites peaks during
the time course (supplemental data). Many of the genes that are transcriptionally
upregulated at 1 hour, such as CD69, are elevated throughout the time examined, although a few genes including PTEN, DUSP5, and NFκB1 itself, show elevated transcription only after 60 minutes. The simultaneous increase
of NFκB1 gene transcription (between 30 minutes and 1 hour) combined with a noticeable increase
in the transcription of genes containing NFκB-binding sites (at one hour) demonstrates
the synchronous relationship between the appearance of this transcription factor and
its downstream targets at the indicated time points.

Figure 9. Enrichment of NFAT and NFκB transcription factor-binding sites in the promoter regions
of genes upregulated after 60 min of treatment of human Jurkat T cells with PMA +
Ionomycin. Color gradient from bright red to bright green is directly proportional
to Z ratios and indicates the increase (red) or decrease (green) of gene expression
relative to baseline at each of the indicated time points.

Discussion

The absence of significant regulation at the transcriptional level during a time in
which gene expression is strongly perturbed at the polyA mRNA level during T cell
activation reveals that many genes are being regulated through changes in stability.
The lack of detectable transcriptional regulation of large numbers of steady-state
mRNA gene changes is particularly striking insofar as the NRO measurements are likely
to be even more sensitive to changes in gene expression than polyA mRNA measurements
since they are a direct measure of newly synthesized mRNA. This finding is consistent
with the work of Raghavan et. al. [12,20] who also noted the presence of numerous transcripts exhibiting stimulus-dependent
changes in mRNA decay in human T lymphocytes treated for 3 hours with anti-CD3 and/or
anti-CD28 antibodies. These experiments were carried out by arresting transcription
with Actinomycin D (in the presence or absence of activation), and mRNA turnover rates
were globally measured by applying polyA mRNA to microarrays. Similarly, in this current
work, patterns of Actinomycin D resistant changes in gene expression among groups
of genes significantly regulated only at the polyA mRNA level supports the observation
that large numbers of genes are regulated primarily by stability during T cell activation.

The smaller, but still substantial, group of genes which displayed significant transcriptional
regulation without any corresponding changes in polyA mRNA, may either be a reflection
of the enhanced sensitivity of transcriptional detection or, perhaps, result from
a persistent lag between changes in transcriptional output and their reflection in
steady-state mRNA levels. Such a lag might arise from differences in scale of the
absolute size of mRNA pools between newly transcribed and polyA mRNA. In this scenario,
changes in the amounts of mRNA that are readily detectable by nuclear run-on may be
too small to have an immediate detectable impact on the steady-state RNA pools, possibly
for long periods of time. Another possibility is that the nascent mRNAs of some of
these genes are so rapidly degraded that they never significantly impact on polyA
mRNA levels at all.

As previously noted (Fig. 6), up-regulation of gene expression was a dominant trend for transcriptionally regulated
genes while down-regulation was dramatically favored for genes regulated at the polyA
mRNA level. From the standpoint of an overall cellular economy of gene expression,
effective control at the level of transcription can be achieved primarily by turning
on new gene transcription, while at the whole-cell level, rapid and effective regulation
can be achieved by massive shifts in the stability of existing mRNA pools. In fact,
the single most dramatic regulatory event experimentally observed was the rapid clearance
of large pools of steady-state mRNA, presumably as result of a sudden and demanding
change in cellular conditions.

Questions remain as to the extent that this type of regulation may occur under differing
biological scenarios. Global regulation of mRNA stability has thus far been systematically
studied under conditions of stress and cellular activation. It remains to be determined
whether or not changes in mRNA stability are responsible for altering gene expression
programs in response to other biological conditions. The existence of these regulatory
paradigms may require a reevaluation of the common model of the control of gene expression
which essentially invokes the turning on and off of gene transcription in order to
explain changes in polyA mRNA levels.

Conclusion

Although stability regulation might reasonably be considered as a cellular measure
to bridge the gap between the very rapid events of signal transduction and the longer
term induction of cellular programs involving the coordinated transcription of batteries
of new gene synthesis, the current data actually suggests otherwise. One of the goals
in this work was to examine changes in gene expression due to altered transcription
and those due to altered turnover across a time course allowing sufficient time for
the resolution of time disparities between new mRNA synthesis and their appearance
as measurable cellular pools. Sufficient time for effective transcription and even
translation was clearly demonstrated by the example of NFKB1 mRNA induction followed
by the transcription of its downstream targets. Patterns of stability regulated genes
were, in this model system, non-random and surprisingly persistent throughout the
entire time course suggesting that stability regulation was a major component in the
control of gene expression for significant periods of time. The presence of highly
active and global regulation of polyA mRNA levels by stability-altering mechanisms
suggests a new significant level of regulation in the control of gene expression which
spans wide phylogenetic distances from yeast [15] to humans, emphasizes a possible parsimonious role for new gene synthesis in response
to changing cellular environments, and may help to explain some of the difficulties
encountered in attempts to comprehensively correlate clustered changes in polyA mRNA
with common promoter regulatory elements.

PolyA mRNA samples were radiolabeled and hybridized as previously described [22]. In brief, 5 μg of total mRNA for each sample was annealed, in 16 μl H2O, with 1 μg of 24-mer poly(dT) primer (Research Genetics, Alabama), by heating at
65°C for 10 min and cooling on ice for 2 min. The RT reaction was performed by adding
8 μl of 5X first strand RT buffer (Life Technologies, Rockville, MD), 4 μl of 20 mM
dNTPs minus dCTP) (Pharmacia, Piscataway, NJ), 4 μl of 0.1 M DTT, 40 U of RNAseOUT
(Life Technologies), 6 μl of 3000 Ci/mmol α-33P dCTP (ICN Biomedicals, Costa Mesa, CA) to the RNA/primer mixture to a final volume
of 40 μl. Two μl (400 U) of Superscript II reverse transcriptase (Life Technologies)
was then added, and the sample was incubated for 60 min at 42°C. The reaction was
stopped by the addition of five μl of 0.5 M EDTA. The samples were incubated at 65°C
for 30 min after addition of 10 μl of 0.1 M NaOH in order to hydrolyze and remove
RNA. The samples were pH neutralized by the addition of 25 μl of 0.5 M Tris, pH 8.0,
and purified using Bio-Rad 6 purification columns (Hercules, CA). An aliquout of each
labled sample was quantitated by liquid scintillation counting using a Beckman LS
6500. The entire remaining sample was stored at -20°C until use.

Array construction and hybridization

Microarray construction and hybridization were previously described [23]. Briefly, NIA Human Focused Arrays consisting of a set of 4600 spotted cDNAs, arrayed
in duplicate, representing a set of 2742 non-redundant genes (enriched in genes involved
in immune function and signal transduction), were printed on Nytran + Supercharge
nylon membranes (Schleicher & Schuell, Keene, NH), and hybridized with [α-33P]dCTP-labeled cDNA or [α-33P]UTP RNA probes overnight at 50°C as previously described [22], protocols available at http://www.grc.nia.nih.gov/branches/rrb/dna/dna.htmwebcite. Hybridized arrays were rinsed in 2 X SSC and 0.1% SDS twice at 55°C followed by
washes in 2 X SSC and 0.1% SDS at 55°C. Microarrays were exposed for 1–3 d and scanned
using a PhosphorImager (Molecular Dynamics, Sunnyvale, CA) at a 50-μm pixel resolution.
ArrayPro software (MediaCybernetics, Silver Spring, MD) was used to convert the hybridization
signals into raw intensity values; data generated were transferred into Microsoft
Excel for further analysis.

PCR analysis

Single endpoint PCR was carried out on polyA mRNA for gene detection and relative
quantitative comparison between baseline (0 time) and I hour activation (with PMA
+I). In brief, GS320 libraries for both control and activated polyA mRNA were prepared
as described [24], normalized using a panel of ribosomal protein genes, and specific genes were amplified
in duplicate using predefined GS320 primers.

Array data analysis

RNA samples (typically n = 3 to n = 5) were prepared from multiple experiments, each
of which consisted of a consecutive series of time points. Equal amounts of total
RNA (5 μg) or NRO RNA (108 cell-equivalents) were used in each hybridization. Raw intensity data for each experiment
was transformed to log10, then used for the calculation of Z scores as described [22] [see 1]. Significant changes in gene expression were calculated in the form of Z ratios
and/or Z test values ([25]), using Z score values in all calculations. Z ratios constitute a measure of the
change in gene expression of a given gene from its baseline value (in this case –
time 0), expressed in units of standard deviation from the average change of all genes
for that comparison. Z ratios are a direct measure of the likelihood that an observed
change is an outlier in an otherwise normal distribution and, as such, are independent
from underlying intensity values. Since the contents of the population of nuclear
run-on and polyA mRNA are different in both complexity and number, care was taken
not to compare Z score normalized intensities directly. Comparisons between Z ratios,
however, test for equivalence of significant changes between the transcriptional and
steady-state changes in gene expression each relative to its own population. All gene
expression changes were assessed through comparison with untreated cells (time 0).
A Z ratio value of ± 1.50 and/or a Z test value p < 0.0001 were the significance thresholds
used in this study.

Hierarchical clustering was performed using the Cluster and TreeView software programs,
developed at Stanford University [26]. The clustering algorithm was set to complete linkage clustering using the uncentered
Pearson correlation.

Mapping of transcription factor binding sites in selected genes was performed using
software from Genomatix Software Gmbh, Munich, Germany http://www.genomatix.dewebcite. See 2 for a complete description of the analysis.

Abbreviations

NRO, nuclear run-on; P+I, PMA plus Ionomycin; ActD, Actinomycin D

Authors' contributions

CC and JF performed the microarray assays (JF-NRO RNA, CC-polyA mRNA). CC carried
out statistical analysis, and drafted the manuscript (with MG & KGB). JR and LD performed
the PCR validation assays. TW assisted with promoter analysis and YSC-C participated
in the design of the study. All authors read and approved the final manuscript.

Acknowledgements

We are grateful to R.L. Wange (NIA, NIH) for kindly providing Jurkat cells.